Recommender systems generate recommendations by analysing which items the user consumes or likes. Moreover, in many scenarios, e.g., when a user is visiting an exhibition or a city, users are faced with a sequence of ...

We present a research tool for user preference elicitation that collects both explicit user feedback and unobtrusively acquired facial expressions. The concrete implementation is a web-based user interface where the user ...

Context, as modeled through variables called contextual factors, can improve human-computer interaction. To date, in applications supporting software development, such as integrated development environments (IDEs) and ...

Group recommender systems (GRSs) support users in a group when facing decision making tasks. Many GRSs simply perform a one shot aggregation of the individual preferences and then compute recommendations. In this paper, ...

In this abstract we discuss how long-Term and discussion-generated preferences can be appropriately combined in supporting group decision making. We measure the quality of a group recommendation model by varying the ...

The Internet of Things (IoT) enables new ways for exploiting the synergy between the physical and the digital world and therefore promises a more direct and active interaction between tourists and local products and places. ...

Music and places can both trigger emotional responses in people. This chapter presents a technical approach that exploits the congruence of emotions raised by music and places to identify music tracks that match a place ...

Traditional food recommender systems exploit items' ratings and descriptions in order to generate relevant recommendations for the users. While this data is important, it might not entirely capture the true users' preferences. ...

Context: A set of algorithms exist to generate integrated development environment (IDE) command recommendations. The recommendations are aimed at improving software developer’s interaction with an IDE. Even though the ...